ArticleBMC medical informatics and decision making2021
Machine learning approaches for the prediction of postoperative complication risk in liver resection patients.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Development and internal validation of a machine learning-based model for predicting postoperative complications after primary liver cancer resection.BMC surgery · 2026Article
- Interpretable machine learning model predicts postoperative complications after thoracoscopic mediastinal tumor surgery: a multicenter study.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence in Minimally Invasive and Robotic Gastrointestinal Surgery: Major Applications and Recent Advances.Journal of personalized medicine · 2026Review
- PRESCO: an online tool for predicting severe pulmonary complications and survival after cancer surgery.Frontiers in oncology · 2025Article
- Association Between Social Capital and Anxiety Among Older Adults in China: A Cross-Sectional Study.Psychology research and behavior management · 2025Article
- Predictors for 30-day mortality in hepatocellular carcinoma patients undergoing liver resection.Narra J · 2024Article
- Innovation and challenges of artificial intelligence technology in personalized healthcare.Scientific reports · 2024Review
- Outcome prediction after resection of colorectal cancer liver metastases: out with the old, in with the new?Hepatobiliary surgery and nutrition · 2024Article
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.Journal of clinical medicine · 2024Article
- Machine learning approach for the detection of vitamin D level: a comparative study.BMC medical informatics and decision making · 2023Article
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions.
objectiveThe aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications.
methodsThe study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications.
resultsRandom Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77-1), with an accuracy of 92.45% (95% CI 85-100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient's BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients.
conclusionsTo reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.